Overview

Dataset statistics

Number of variables87
Number of observations176
Missing cells38
Missing cells (%)0.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory119.8 KiB
Average record size in memory696.7 B

Variable types

Numeric6
Categorical81

Alerts

EstateType has constant value ""Constant
DistributionType has constant value ""Constant
Object_price is highly overall correlated with LivingSpace and 12 other fieldsHigh correlation
LivingSpace is highly overall correlated with Object_price and 6 other fieldsHigh correlation
Rooms is highly overall correlated with Object_price and 2 other fieldsHigh correlation
ConstructionYear is highly overall correlated with bidet and 5 other fieldsHigh correlation
abstellraum is highly overall correlated with kable_sat_tv and 1 other fieldsHigh correlation
bidet is highly overall correlated with Object_price and 2 other fieldsHigh correlation
carport is highly overall correlated with Object_priceHigh correlation
dusche is highly overall correlated with fliesenHigh correlation
elektro is highly overall correlated with Object_price and 4 other fieldsHigh correlation
erdwaerme is highly overall correlated with Object_price and 6 other fieldsHigh correlation
fliesen is highly overall correlated with dusche and 1 other fieldsHigh correlation
frei is highly overall correlated with kunststofffensterHigh correlation
fu\u00dfbodenheizung is highly overall correlated with kontrollierte_be-_und_entl\u00fcftungsanlageHigh correlation
gaestewc is highly overall correlated with LivingSpaceHigh correlation
garage is highly overall correlated with Object_price and 3 other fieldsHigh correlation
kable_sat_tv is highly overall correlated with abstellraum and 1 other fieldsHigh correlation
kamera is highly overall correlated with ConstructionYearHigh correlation
kfw40 is highly overall correlated with ConstructionYearHigh correlation
kontrollierte_be-_und_entl\u00fcftungsanlage is highly overall correlated with Object_price and 4 other fieldsHigh correlation
kunststofffenster is highly overall correlated with fliesen and 1 other fieldsHigh correlation
luftwp is highly overall correlated with Object_price and 2 other fieldsHigh correlation
ofenheizung is highly overall correlated with ConstructionYearHigh correlation
pantry is highly overall correlated with ConstructionYearHigh correlation
parkett is highly overall correlated with Object_price and 1 other fieldsHigh correlation
reinigung is highly overall correlated with Object_price and 5 other fieldsHigh correlation
rollstuhlgerecht is highly overall correlated with Object_price and 1 other fieldsHigh correlation
speisekammer is highly overall correlated with Object_price and 1 other fieldsHigh correlation
stein is highly overall correlated with ConstructionYearHigh correlation
als_ferienimmobilie_geeignet is highly imbalanced (94.9%)Imbalance
altbau_(bis_1945) is highly imbalanced (68.5%)Imbalance
bad/wc_getrennt is highly imbalanced (78.5%)Imbalance
barriefrei is highly imbalanced (64.1%)Imbalance
bidet is highly imbalanced (94.9%)Imbalance
carport is highly imbalanced (94.9%)Imbalance
dachgeschoss is highly imbalanced (54.2%)Imbalance
dielen is highly imbalanced (91.0%)Imbalance
dsl is highly imbalanced (64.1%)Imbalance
duplex is highly imbalanced (91.0%)Imbalance
elektro is highly imbalanced (64.1%)Imbalance
erdgeschoss is highly imbalanced (54.2%)Imbalance
erdwaerme is highly imbalanced (75.9%)Imbalance
erstbezug is highly imbalanced (68.5%)Imbalance
estrich is highly imbalanced (94.9%)Imbalance
etagenheizung is highly imbalanced (50.6%)Imbalance
fern is highly imbalanced (59.9%)Imbalance
ferne is highly imbalanced (52.4%)Imbalance
garage is highly imbalanced (58.0%)Imbalance
garten is highly imbalanced (56.1%)Imbalance
gartennutzung is highly imbalanced (64.1%)Imbalance
haustiere_erlaubt is highly imbalanced (81.4%)Imbalance
holzfenster is highly imbalanced (64.1%)Imbalance
kamera is highly imbalanced (91.0%)Imbalance
kamin is highly imbalanced (87.5%)Imbalance
kfw40 is highly imbalanced (91.0%)Imbalance
kfw55 is highly imbalanced (94.9%)Imbalance
kfw70 is highly imbalanced (94.9%)Imbalance
klimatisiert is highly imbalanced (91.0%)Imbalance
kontrollierte_be-_und_entl\u00fcftungsanlage is highly imbalanced (59.9%)Imbalance
kunststoff is highly imbalanced (78.5%)Imbalance
linoleum is highly imbalanced (84.4%)Imbalance
loggia is highly imbalanced (84.4%)Imbalance
luftwp is highly imbalanced (50.6%)Imbalance
marmor is highly imbalanced (94.9%)Imbalance
massivhaus is highly imbalanced (81.4%)Imbalance
neuwertig is highly imbalanced (81.4%)Imbalance
oel is highly imbalanced (58.0%)Imbalance
ofenheizung is highly imbalanced (94.9%)Imbalance
pantry is highly imbalanced (94.9%)Imbalance
pellet is highly imbalanced (91.0%)Imbalance
reinigung is highly imbalanced (56.1%)Imbalance
rollstuhlgerecht is highly imbalanced (91.0%)Imbalance
sat is highly imbalanced (75.9%)Imbalance
speisekammer is highly imbalanced (87.5%)Imbalance
stein is highly imbalanced (91.0%)Imbalance
teilweise_m\u00f6bliert is highly imbalanced (81.4%)Imbalance
teppich is highly imbalanced (87.5%)Imbalance
tiefgarage is highly imbalanced (66.3%)Imbalance
vermietet is highly imbalanced (87.5%)Imbalance
wasch_trockenraum is highly imbalanced (64.1%)Imbalance
wg_geeignet is highly imbalanced (68.5%)Imbalance
wintergarten is highly imbalanced (84.4%)Imbalance
wohnberechtigungsschein is highly imbalanced (87.5%)Imbalance
ConstructionYear has 38 (21.6%) missing valuesMissing
Unnamed: 0 is uniformly distributedUniform
Unnamed: 0 has unique valuesUnique

Reproduction

Analysis started2023-07-11 14:32:17.746559
Analysis finished2023-07-11 14:32:52.128986
Duration34.38 seconds
Software versionydata-profiling vv4.3.1
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct176
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean87.5
Minimum0
Maximum175
Zeros1
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-07-11T16:32:52.292443image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8.75
Q143.75
median87.5
Q3131.25
95-th percentile166.25
Maximum175
Range175
Interquartile range (IQR)87.5

Descriptive statistics

Standard deviation50.950957
Coefficient of variation (CV)0.58229665
Kurtosis-1.2
Mean87.5
Median Absolute Deviation (MAD)44
Skewness0
Sum15400
Variance2596
MonotonicityStrictly increasing
2023-07-11T16:32:52.486150image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
0.6%
1 1
 
0.6%
112 1
 
0.6%
113 1
 
0.6%
114 1
 
0.6%
115 1
 
0.6%
116 1
 
0.6%
117 1
 
0.6%
118 1
 
0.6%
119 1
 
0.6%
Other values (166) 166
94.3%
ValueCountFrequency (%)
0 1
0.6%
1 1
0.6%
2 1
0.6%
3 1
0.6%
4 1
0.6%
5 1
0.6%
6 1
0.6%
7 1
0.6%
8 1
0.6%
9 1
0.6%
ValueCountFrequency (%)
175 1
0.6%
174 1
0.6%
173 1
0.6%
172 1
0.6%
171 1
0.6%
170 1
0.6%
169 1
0.6%
168 1
0.6%
167 1
0.6%
166 1
0.6%

Object_price
Real number (ℝ)

HIGH CORRELATION 

Distinct127
Distinct (%)72.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean926.10239
Minimum220
Maximum3601.44
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-07-11T16:32:52.680126image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum220
5-th percentile328.75
Q1577.5
median800
Q31096.25
95-th percentile1961.25
Maximum3601.44
Range3381.44
Interquartile range (IQR)518.75

Descriptive statistics

Standard deviation548.58475
Coefficient of variation (CV)0.59235864
Kurtosis5.9063023
Mean926.10239
Median Absolute Deviation (MAD)245.005
Skewness2.0805872
Sum162994.02
Variance300945.23
MonotonicityNot monotonic
2023-07-11T16:32:52.858688image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
750 5
 
2.8%
1500 4
 
2.3%
900 4
 
2.3%
1100 4
 
2.3%
560 3
 
1.7%
450 3
 
1.7%
800 3
 
1.7%
990 3
 
1.7%
680 3
 
1.7%
340 3
 
1.7%
Other values (117) 141
80.1%
ValueCountFrequency (%)
220 2
1.1%
265 1
 
0.6%
275 1
 
0.6%
285 1
 
0.6%
300 1
 
0.6%
310 1
 
0.6%
320 1
 
0.6%
325 1
 
0.6%
330 2
1.1%
340 3
1.7%
ValueCountFrequency (%)
3601.44 1
0.6%
3163.32 1
0.6%
3030.93 1
0.6%
2880 1
0.6%
2545.6 1
0.6%
2355 1
0.6%
2220 2
1.1%
2175 1
0.6%
1890 1
0.6%
1797.71 1
0.6%

LivingSpace
Real number (ℝ)

HIGH CORRELATION 

Distinct116
Distinct (%)65.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.585682
Minimum9.6
Maximum225.09
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-07-11T16:32:53.059248image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum9.6
5-th percentile15.2275
Q148
median68
Q388.25
95-th percentile150.825
Maximum225.09
Range215.49
Interquartile range (IQR)40.25

Descriptive statistics

Standard deviation40.462779
Coefficient of variation (CV)0.56523565
Kurtosis3.0240573
Mean71.585682
Median Absolute Deviation (MAD)20
Skewness1.3632288
Sum12599.08
Variance1637.2365
MonotonicityNot monotonic
2023-07-11T16:32:53.447097image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
77 5
 
2.8%
55 5
 
2.8%
90 4
 
2.3%
70 4
 
2.3%
75 4
 
2.3%
65 4
 
2.3%
50 4
 
2.3%
79 3
 
1.7%
85 3
 
1.7%
28 3
 
1.7%
Other values (106) 137
77.8%
ValueCountFrequency (%)
9.6 1
0.6%
9.8 1
0.6%
10 1
0.6%
11.7 1
0.6%
14.16 2
1.1%
14.38 1
0.6%
15.1 1
0.6%
15.13 1
0.6%
15.26 2
1.1%
18 1
0.6%
ValueCountFrequency (%)
225.09 1
0.6%
218.16 1
0.6%
213 2
1.1%
195 1
0.6%
178.29 1
0.6%
170 1
0.6%
159.1 1
0.6%
153.3 1
0.6%
150 2
1.1%
145 1
0.6%

Rooms
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4289773
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-07-11T16:32:53.599558image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q33
95-th percentile5
Maximum6
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1272272
Coefficient of variation (CV)0.46407484
Kurtosis0.86439333
Mean2.4289773
Median Absolute Deviation (MAD)1
Skewness0.86586981
Sum427.5
Variance1.2706412
MonotonicityNot monotonic
2023-07-11T16:32:53.749350image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2 58
33.0%
3 48
27.3%
1 35
19.9%
4 11
 
6.2%
5 6
 
3.4%
1.5 5
 
2.8%
3.5 5
 
2.8%
2.5 4
 
2.3%
6 3
 
1.7%
5.5 1
 
0.6%
ValueCountFrequency (%)
1 35
19.9%
1.5 5
 
2.8%
2 58
33.0%
2.5 4
 
2.3%
3 48
27.3%
3.5 5
 
2.8%
4 11
 
6.2%
5 6
 
3.4%
5.5 1
 
0.6%
6 3
 
1.7%
ValueCountFrequency (%)
6 3
 
1.7%
5.5 1
 
0.6%
5 6
 
3.4%
4 11
 
6.2%
3.5 5
 
2.8%
3 48
27.3%
2.5 4
 
2.3%
2 58
33.0%
1.5 5
 
2.8%
1 35
19.9%

ConstructionYear
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct57
Distinct (%)41.3%
Missing38
Missing (%)21.6%
Infinite0
Infinite (%)0.0%
Mean1986
Minimum1708
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-07-11T16:32:53.918586image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1708
5-th percentile1920
Q11970
median1990
Q32019
95-th percentile2023
Maximum2023
Range315
Interquartile range (IQR)49

Descriptive statistics

Standard deviation39.37523
Coefficient of variation (CV)0.0198264
Kurtosis17.149836
Mean1986
Median Absolute Deviation (MAD)26
Skewness-2.9278148
Sum274068
Variance1550.4088
MonotonicityNot monotonic
2023-07-11T16:32:54.104265image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2022 12
 
6.8%
2019 11
 
6.2%
2023 8
 
4.5%
1970 7
 
4.0%
1980 5
 
2.8%
2015 5
 
2.8%
1974 4
 
2.3%
2020 4
 
2.3%
1985 4
 
2.3%
1950 4
 
2.3%
Other values (47) 74
42.0%
(Missing) 38
21.6%
ValueCountFrequency (%)
1708 1
0.6%
1895 1
0.6%
1900 2
1.1%
1908 1
0.6%
1915 1
0.6%
1920 2
1.1%
1929 1
0.6%
1930 1
0.6%
1934 1
0.6%
1935 1
0.6%
ValueCountFrequency (%)
2023 8
4.5%
2022 12
6.8%
2021 3
 
1.7%
2020 4
 
2.3%
2019 11
6.2%
2018 2
 
1.1%
2017 1
 
0.6%
2016 2
 
1.1%
2015 5
2.8%
2013 1
 
0.6%

ZipCode
Real number (ℝ)

Distinct25
Distinct (%)14.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean97129.324
Minimum97070
Maximum97299
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-07-11T16:32:54.273898image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum97070
5-th percentile97070
Q197074
median97080
Q397218
95-th percentile97276.75
Maximum97299
Range229
Interquartile range (IQR)144

Descriptive statistics

Standard deviation79.244668
Coefficient of variation (CV)0.00081586759
Kurtosis-0.89712807
Mean97129.324
Median Absolute Deviation (MAD)8
Skewness0.92073731
Sum17094761
Variance6279.7174
MonotonicityNot monotonic
2023-07-11T16:32:54.429549image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
97074 26
14.8%
97070 25
14.2%
97078 15
8.5%
97082 15
8.5%
97084 11
 
6.2%
97218 11
 
6.2%
97080 11
 
6.2%
97072 10
 
5.7%
97204 8
 
4.5%
97249 7
 
4.0%
Other values (15) 37
21.0%
ValueCountFrequency (%)
97070 25
14.2%
97072 10
 
5.7%
97074 26
14.8%
97076 6
 
3.4%
97078 15
8.5%
97080 11
6.2%
97082 15
8.5%
97084 11
6.2%
97204 8
 
4.5%
97209 4
 
2.3%
ValueCountFrequency (%)
97299 4
2.3%
97297 4
2.3%
97288 1
 
0.6%
97273 1
 
0.6%
97270 2
 
1.1%
97265 1
 
0.6%
97261 2
 
1.1%
97250 1
 
0.6%
97249 7
4.0%
97246 2
 
1.1%

EstateType
Categorical

CONSTANT 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
APARTMENT
176 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters1584
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAPARTMENT
2nd rowAPARTMENT
3rd rowAPARTMENT
4th rowAPARTMENT
5th rowAPARTMENT

Common Values

ValueCountFrequency (%)
APARTMENT 176
100.0%

Length

2023-07-11T16:32:54.581480image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T16:32:54.753710image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
apartment 176
100.0%

Most occurring characters

ValueCountFrequency (%)
A 352
22.2%
T 352
22.2%
P 176
11.1%
R 176
11.1%
M 176
11.1%
E 176
11.1%
N 176
11.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1584
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 352
22.2%
T 352
22.2%
P 176
11.1%
R 176
11.1%
M 176
11.1%
E 176
11.1%
N 176
11.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 1584
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 352
22.2%
T 352
22.2%
P 176
11.1%
R 176
11.1%
M 176
11.1%
E 176
11.1%
N 176
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1584
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 352
22.2%
T 352
22.2%
P 176
11.1%
R 176
11.1%
M 176
11.1%
E 176
11.1%
N 176
11.1%

DistributionType
Categorical

CONSTANT 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
RENT
176 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters704
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRENT
2nd rowRENT
3rd rowRENT
4th rowRENT
5th rowRENT

Common Values

ValueCountFrequency (%)
RENT 176
100.0%

Length

2023-07-11T16:32:54.880146image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T16:32:55.040201image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
rent 176
100.0%

Most occurring characters

ValueCountFrequency (%)
R 176
25.0%
E 176
25.0%
N 176
25.0%
T 176
25.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 704
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 176
25.0%
E 176
25.0%
N 176
25.0%
T 176
25.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 704
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 176
25.0%
E 176
25.0%
N 176
25.0%
T 176
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 704
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 176
25.0%
E 176
25.0%
N 176
25.0%
T 176
25.0%

abstellraum
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
0
147 
1
29 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 147
83.5%
1 29
 
16.5%

Length

2023-07-11T16:32:55.173161image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T16:32:55.338881image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 147
83.5%
1 29
 
16.5%

Most occurring characters

ValueCountFrequency (%)
0 147
83.5%
1 29
 
16.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 147
83.5%
1 29
 
16.5%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 147
83.5%
1 29
 
16.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 147
83.5%
1 29
 
16.5%

als_ferienimmobilie_geeignet
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
0
175 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.6%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

Length

2023-07-11T16:32:55.484155image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T16:32:55.649952image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

altbau_(bis_1945)
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
0
166 
1
 
10

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 166
94.3%
1 10
 
5.7%

Length

2023-07-11T16:32:55.783412image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T16:32:55.939359image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 166
94.3%
1 10
 
5.7%

Most occurring characters

ValueCountFrequency (%)
0 166
94.3%
1 10
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 166
94.3%
1 10
 
5.7%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 166
94.3%
1 10
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 166
94.3%
1 10
 
5.7%

bad/wc_getrennt
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
0
170 
1
 
6

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 170
96.6%
1 6
 
3.4%

Length

2023-07-11T16:32:56.073766image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T16:32:56.232713image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 170
96.6%
1 6
 
3.4%

Most occurring characters

ValueCountFrequency (%)
0 170
96.6%
1 6
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 170
96.6%
1 6
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 170
96.6%
1 6
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 170
96.6%
1 6
 
3.4%

balkon
Categorical

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
0
104 
1
72 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 104
59.1%
1 72
40.9%

Length

2023-07-11T16:32:56.364307image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T16:32:56.522616image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 104
59.1%
1 72
40.9%

Most occurring characters

ValueCountFrequency (%)
0 104
59.1%
1 72
40.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 104
59.1%
1 72
40.9%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 104
59.1%
1 72
40.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 104
59.1%
1 72
40.9%

barriefrei
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
0
164 
1
 
12

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 164
93.2%
1 12
 
6.8%

Length

2023-07-11T16:32:56.659027image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T16:32:56.819230image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 164
93.2%
1 12
 
6.8%

Most occurring characters

ValueCountFrequency (%)
0 164
93.2%
1 12
 
6.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 164
93.2%
1 12
 
6.8%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 164
93.2%
1 12
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 164
93.2%
1 12
 
6.8%

bidet
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
0
175 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.6%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

Length

2023-07-11T16:32:56.950137image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T16:32:57.111046image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

carport
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
0
175 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.6%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

Length

2023-07-11T16:32:57.241756image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T16:32:57.398203image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

dachgeschoss
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
0
159 
1
17 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 159
90.3%
1 17
 
9.7%

Length

2023-07-11T16:32:57.534213image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T16:32:57.698876image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 159
90.3%
1 17
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 159
90.3%
1 17
 
9.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 159
90.3%
1 17
 
9.7%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 159
90.3%
1 17
 
9.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 159
90.3%
1 17
 
9.7%

dielen
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
0
174 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 174
98.9%
1 2
 
1.1%

Length

2023-07-11T16:32:57.838895image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T16:32:57.997806image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 174
98.9%
1 2
 
1.1%

Most occurring characters

ValueCountFrequency (%)
0 174
98.9%
1 2
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 174
98.9%
1 2
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 174
98.9%
1 2
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 174
98.9%
1 2
 
1.1%

dsl
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
0
164 
1
 
12

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 164
93.2%
1 12
 
6.8%

Length

2023-07-11T16:32:58.133483image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T16:32:58.298028image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 164
93.2%
1 12
 
6.8%

Most occurring characters

ValueCountFrequency (%)
0 164
93.2%
1 12
 
6.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 164
93.2%
1 12
 
6.8%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 164
93.2%
1 12
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 164
93.2%
1 12
 
6.8%

duplex
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
0
174 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 174
98.9%
1 2
 
1.1%

Length

2023-07-11T16:32:58.433579image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T16:32:58.586383image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 174
98.9%
1 2
 
1.1%

Most occurring characters

ValueCountFrequency (%)
0 174
98.9%
1 2
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 174
98.9%
1 2
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 174
98.9%
1 2
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 174
98.9%
1 2
 
1.1%

dusche
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
0
102 
1
74 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 102
58.0%
1 74
42.0%

Length

2023-07-11T16:32:58.718290image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T16:32:58.892957image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 102
58.0%
1 74
42.0%

Most occurring characters

ValueCountFrequency (%)
0 102
58.0%
1 74
42.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 102
58.0%
1 74
42.0%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 102
58.0%
1 74
42.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 102
58.0%
1 74
42.0%

einbauk\u00fcche
Categorical

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
0
92 
1
84 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 92
52.3%
1 84
47.7%

Length

2023-07-11T16:32:59.086153image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T16:32:59.308879image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 92
52.3%
1 84
47.7%

Most occurring characters

ValueCountFrequency (%)
0 92
52.3%
1 84
47.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 92
52.3%
1 84
47.7%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 92
52.3%
1 84
47.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 92
52.3%
1 84
47.7%

elektro
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
0
164 
1
 
12

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 164
93.2%
1 12
 
6.8%

Length

2023-07-11T16:32:59.504117image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T16:32:59.730730image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 164
93.2%
1 12
 
6.8%

Most occurring characters

ValueCountFrequency (%)
0 164
93.2%
1 12
 
6.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 164
93.2%
1 12
 
6.8%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 164
93.2%
1 12
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 164
93.2%
1 12
 
6.8%

erdgeschoss
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
0
159 
1
17 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 159
90.3%
1 17
 
9.7%

Length

2023-07-11T16:32:59.905830image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T16:33:00.128945image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 159
90.3%
1 17
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 159
90.3%
1 17
 
9.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 159
90.3%
1 17
 
9.7%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 159
90.3%
1 17
 
9.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 159
90.3%
1 17
 
9.7%

erdwaerme
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
0
169 
1
 
7

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 169
96.0%
1 7
 
4.0%

Length

2023-07-11T16:33:00.300846image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T16:33:00.468163image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 169
96.0%
1 7
 
4.0%

Most occurring characters

ValueCountFrequency (%)
0 169
96.0%
1 7
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 169
96.0%
1 7
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 169
96.0%
1 7
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 169
96.0%
1 7
 
4.0%

erstbezug
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
0
166 
1
 
10

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 166
94.3%
1 10
 
5.7%

Length

2023-07-11T16:33:00.615964image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T16:33:00.784326image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 166
94.3%
1 10
 
5.7%

Most occurring characters

ValueCountFrequency (%)
0 166
94.3%
1 10
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 166
94.3%
1 10
 
5.7%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 166
94.3%
1 10
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 166
94.3%
1 10
 
5.7%

estrich
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
0
175 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.6%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

Length

2023-07-11T16:33:00.919987image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T16:33:01.078868image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

etagenheizung
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
0
157 
1
19 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 157
89.2%
1 19
 
10.8%

Length

2023-07-11T16:33:01.210036image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T16:33:01.371699image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 157
89.2%
1 19
 
10.8%

Most occurring characters

ValueCountFrequency (%)
0 157
89.2%
1 19
 
10.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 157
89.2%
1 19
 
10.8%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 157
89.2%
1 19
 
10.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 157
89.2%
1 19
 
10.8%

fenster
Categorical

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
0
100 
1
76 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 100
56.8%
1 76
43.2%

Length

2023-07-11T16:33:01.754668image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T16:33:01.915326image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 100
56.8%
1 76
43.2%

Most occurring characters

ValueCountFrequency (%)
0 100
56.8%
1 76
43.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 100
56.8%
1 76
43.2%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 100
56.8%
1 76
43.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 100
56.8%
1 76
43.2%

fern
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
0
162 
1
 
14

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 162
92.0%
1 14
 
8.0%

Length

2023-07-11T16:33:02.053721image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T16:33:02.223863image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 162
92.0%
1 14
 
8.0%

Most occurring characters

ValueCountFrequency (%)
0 162
92.0%
1 14
 
8.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 162
92.0%
1 14
 
8.0%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 162
92.0%
1 14
 
8.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 162
92.0%
1 14
 
8.0%

ferne
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
0
158 
1
18 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 158
89.8%
1 18
 
10.2%

Length

2023-07-11T16:33:02.369292image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T16:33:02.529789image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 158
89.8%
1 18
 
10.2%

Most occurring characters

ValueCountFrequency (%)
0 158
89.8%
1 18
 
10.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 158
89.8%
1 18
 
10.2%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 158
89.8%
1 18
 
10.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 158
89.8%
1 18
 
10.2%

fliesen
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
0
112 
1
64 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 112
63.6%
1 64
36.4%

Length

2023-07-11T16:33:02.664182image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T16:33:02.820106image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 112
63.6%
1 64
36.4%

Most occurring characters

ValueCountFrequency (%)
0 112
63.6%
1 64
36.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 112
63.6%
1 64
36.4%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 112
63.6%
1 64
36.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 112
63.6%
1 64
36.4%

frei
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
0
146 
1
30 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 146
83.0%
1 30
 
17.0%

Length

2023-07-11T16:33:02.955101image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T16:33:03.111993image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 146
83.0%
1 30
 
17.0%

Most occurring characters

ValueCountFrequency (%)
0 146
83.0%
1 30
 
17.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 146
83.0%
1 30
 
17.0%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 146
83.0%
1 30
 
17.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 146
83.0%
1 30
 
17.0%

fu\u00dfbodenheizung
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
0
143 
1
33 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 143
81.2%
1 33
 
18.8%

Length

2023-07-11T16:33:03.245631image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T16:33:03.407374image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 143
81.2%
1 33
 
18.8%

Most occurring characters

ValueCountFrequency (%)
0 143
81.2%
1 33
 
18.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 143
81.2%
1 33
 
18.8%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 143
81.2%
1 33
 
18.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 143
81.2%
1 33
 
18.8%

gaestewc
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
0
145 
1
31 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 145
82.4%
1 31
 
17.6%

Length

2023-07-11T16:33:03.545385image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T16:33:03.703518image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 145
82.4%
1 31
 
17.6%

Most occurring characters

ValueCountFrequency (%)
0 145
82.4%
1 31
 
17.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 145
82.4%
1 31
 
17.6%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 145
82.4%
1 31
 
17.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 145
82.4%
1 31
 
17.6%

garage
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
0
161 
1
 
15

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 161
91.5%
1 15
 
8.5%

Length

2023-07-11T16:33:03.839478image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T16:33:04.006268image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 161
91.5%
1 15
 
8.5%

Most occurring characters

ValueCountFrequency (%)
0 161
91.5%
1 15
 
8.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 161
91.5%
1 15
 
8.5%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 161
91.5%
1 15
 
8.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 161
91.5%
1 15
 
8.5%

garten
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
0
160 
1
 
16

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 160
90.9%
1 16
 
9.1%

Length

2023-07-11T16:33:04.148973image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T16:33:04.303170image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 160
90.9%
1 16
 
9.1%

Most occurring characters

ValueCountFrequency (%)
0 160
90.9%
1 16
 
9.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 160
90.9%
1 16
 
9.1%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 160
90.9%
1 16
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 160
90.9%
1 16
 
9.1%

gartennutzung
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
0
164 
1
 
12

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 164
93.2%
1 12
 
6.8%

Length

2023-07-11T16:33:04.437278image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T16:33:04.599439image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 164
93.2%
1 12
 
6.8%

Most occurring characters

ValueCountFrequency (%)
0 164
93.2%
1 12
 
6.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 164
93.2%
1 12
 
6.8%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 164
93.2%
1 12
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 164
93.2%
1 12
 
6.8%

gas
Categorical

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
0
102 
1
74 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 102
58.0%
1 74
42.0%

Length

2023-07-11T16:33:04.734033image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T16:33:04.889292image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 102
58.0%
1 74
42.0%

Most occurring characters

ValueCountFrequency (%)
0 102
58.0%
1 74
42.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 102
58.0%
1 74
42.0%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 102
58.0%
1 74
42.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 102
58.0%
1 74
42.0%

gepflegt
Categorical

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
0
150 
1
26 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 150
85.2%
1 26
 
14.8%

Length

2023-07-11T16:33:05.026655image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T16:33:05.188233image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 150
85.2%
1 26
 
14.8%

Most occurring characters

ValueCountFrequency (%)
0 150
85.2%
1 26
 
14.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 150
85.2%
1 26
 
14.8%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 150
85.2%
1 26
 
14.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 150
85.2%
1 26
 
14.8%

haustiere_erlaubt
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
0
171 
1
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 171
97.2%
1 5
 
2.8%

Length

2023-07-11T16:33:05.321579image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T16:33:05.479722image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 171
97.2%
1 5
 
2.8%

Most occurring characters

ValueCountFrequency (%)
0 171
97.2%
1 5
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 171
97.2%
1 5
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 171
97.2%
1 5
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 171
97.2%
1 5
 
2.8%

holzfenster
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
0
164 
1
 
12

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 164
93.2%
1 12
 
6.8%

Length

2023-07-11T16:33:05.619114image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T16:33:05.786143image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 164
93.2%
1 12
 
6.8%

Most occurring characters

ValueCountFrequency (%)
0 164
93.2%
1 12
 
6.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 164
93.2%
1 12
 
6.8%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 164
93.2%
1 12
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 164
93.2%
1 12
 
6.8%

kable_sat_tv
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
0
145 
1
31 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 145
82.4%
1 31
 
17.6%

Length

2023-07-11T16:33:05.920980image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T16:33:06.084101image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 145
82.4%
1 31
 
17.6%

Most occurring characters

ValueCountFrequency (%)
0 145
82.4%
1 31
 
17.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 145
82.4%
1 31
 
17.6%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 145
82.4%
1 31
 
17.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 145
82.4%
1 31
 
17.6%

kamera
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
0
174 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 174
98.9%
1 2
 
1.1%

Length

2023-07-11T16:33:06.229860image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T16:33:06.396443image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 174
98.9%
1 2
 
1.1%

Most occurring characters

ValueCountFrequency (%)
0 174
98.9%
1 2
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 174
98.9%
1 2
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 174
98.9%
1 2
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 174
98.9%
1 2
 
1.1%

kamin
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
0
173 
1
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 173
98.3%
1 3
 
1.7%

Length

2023-07-11T16:33:06.533842image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T16:33:06.689684image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 173
98.3%
1 3
 
1.7%

Most occurring characters

ValueCountFrequency (%)
0 173
98.3%
1 3
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 173
98.3%
1 3
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 173
98.3%
1 3
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 173
98.3%
1 3
 
1.7%

kelleranteil
Categorical

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
0
90 
1
86 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 90
51.1%
1 86
48.9%

Length

2023-07-11T16:33:06.818800image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T16:33:06.975348image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 90
51.1%
1 86
48.9%

Most occurring characters

ValueCountFrequency (%)
0 90
51.1%
1 86
48.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 90
51.1%
1 86
48.9%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 90
51.1%
1 86
48.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 90
51.1%
1 86
48.9%

kfw40
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
0
174 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 174
98.9%
1 2
 
1.1%

Length

2023-07-11T16:33:07.112902image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T16:33:07.271481image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 174
98.9%
1 2
 
1.1%

Most occurring characters

ValueCountFrequency (%)
0 174
98.9%
1 2
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 174
98.9%
1 2
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 174
98.9%
1 2
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 174
98.9%
1 2
 
1.1%

kfw55
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
0
175 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.6%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

Length

2023-07-11T16:33:07.402333image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T16:33:07.556902image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

kfw70
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
0
175 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.6%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

Length

2023-07-11T16:33:07.686411image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T16:33:07.846529image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

klimatisiert
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
0
174 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 174
98.9%
1 2
 
1.1%

Length

2023-07-11T16:33:07.974713image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T16:33:08.131430image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 174
98.9%
1 2
 
1.1%

Most occurring characters

ValueCountFrequency (%)
0 174
98.9%
1 2
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 174
98.9%
1 2
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 174
98.9%
1 2
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 174
98.9%
1 2
 
1.1%

kontrollierte_be-_und_entl\u00fcftungsanlage
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
0
162 
1
 
14

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 162
92.0%
1 14
 
8.0%

Length

2023-07-11T16:33:08.261812image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T16:33:08.418744image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 162
92.0%
1 14
 
8.0%

Most occurring characters

ValueCountFrequency (%)
0 162
92.0%
1 14
 
8.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 162
92.0%
1 14
 
8.0%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 162
92.0%
1 14
 
8.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 162
92.0%
1 14
 
8.0%

kunststoff
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
0
170 
1
 
6

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 170
96.6%
1 6
 
3.4%

Length

2023-07-11T16:33:08.551296image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T16:33:08.707428image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 170
96.6%
1 6
 
3.4%

Most occurring characters

ValueCountFrequency (%)
0 170
96.6%
1 6
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 170
96.6%
1 6
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 170
96.6%
1 6
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 170
96.6%
1 6
 
3.4%

kunststofffenster
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
0
137 
1
39 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 137
77.8%
1 39
 
22.2%

Length

2023-07-11T16:33:08.839642image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T16:33:09.006569image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 137
77.8%
1 39
 
22.2%

Most occurring characters

ValueCountFrequency (%)
0 137
77.8%
1 39
 
22.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 137
77.8%
1 39
 
22.2%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 137
77.8%
1 39
 
22.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 137
77.8%
1 39
 
22.2%

laminat
Categorical

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
0
147 
1
29 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 147
83.5%
1 29
 
16.5%

Length

2023-07-11T16:33:09.142668image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T16:33:09.313752image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 147
83.5%
1 29
 
16.5%

Most occurring characters

ValueCountFrequency (%)
0 147
83.5%
1 29
 
16.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 147
83.5%
1 29
 
16.5%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 147
83.5%
1 29
 
16.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 147
83.5%
1 29
 
16.5%

linoleum
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
0
172 
1
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 172
97.7%
1 4
 
2.3%

Length

2023-07-11T16:33:09.457375image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T16:33:09.615205image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 172
97.7%
1 4
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 172
97.7%
1 4
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 172
97.7%
1 4
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 172
97.7%
1 4
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 172
97.7%
1 4
 
2.3%

loggia
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
0
172 
1
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 172
97.7%
1 4
 
2.3%

Length

2023-07-11T16:33:09.748203image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T16:33:09.905326image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 172
97.7%
1 4
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 172
97.7%
1 4
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 172
97.7%
1 4
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 172
97.7%
1 4
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 172
97.7%
1 4
 
2.3%

luftwp
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
0
157 
1
19 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 157
89.2%
1 19
 
10.8%

Length

2023-07-11T16:33:10.035257image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T16:33:10.189205image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 157
89.2%
1 19
 
10.8%

Most occurring characters

ValueCountFrequency (%)
0 157
89.2%
1 19
 
10.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 157
89.2%
1 19
 
10.8%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 157
89.2%
1 19
 
10.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 157
89.2%
1 19
 
10.8%

marmor
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
0
175 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.6%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

Length

2023-07-11T16:33:10.321435image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T16:33:10.479250image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

massivhaus
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
0
171 
1
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 171
97.2%
1 5
 
2.8%

Length

2023-07-11T16:33:10.607650image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T16:33:10.760055image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 171
97.2%
1 5
 
2.8%

Most occurring characters

ValueCountFrequency (%)
0 171
97.2%
1 5
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 171
97.2%
1 5
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 171
97.2%
1 5
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 171
97.2%
1 5
 
2.8%

moebliert
Categorical

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
0
151 
1
25 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 151
85.8%
1 25
 
14.2%

Length

2023-07-11T16:33:10.885643image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T16:33:11.040841image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 151
85.8%
1 25
 
14.2%

Most occurring characters

ValueCountFrequency (%)
0 151
85.8%
1 25
 
14.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 151
85.8%
1 25
 
14.2%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 151
85.8%
1 25
 
14.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 151
85.8%
1 25
 
14.2%

neubau
Categorical

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
0
151 
1
25 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 151
85.8%
1 25
 
14.2%

Length

2023-07-11T16:33:11.176346image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T16:33:11.331917image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 151
85.8%
1 25
 
14.2%

Most occurring characters

ValueCountFrequency (%)
0 151
85.8%
1 25
 
14.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 151
85.8%
1 25
 
14.2%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 151
85.8%
1 25
 
14.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 151
85.8%
1 25
 
14.2%

neuwertig
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
0
171 
1
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 171
97.2%
1 5
 
2.8%

Length

2023-07-11T16:33:11.462854image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T16:33:11.613256image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 171
97.2%
1 5
 
2.8%

Most occurring characters

ValueCountFrequency (%)
0 171
97.2%
1 5
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 171
97.2%
1 5
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 171
97.2%
1 5
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 171
97.2%
1 5
 
2.8%

oel
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
0
161 
1
 
15

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 161
91.5%
1 15
 
8.5%

Length

2023-07-11T16:33:12.018950image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T16:33:12.172490image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 161
91.5%
1 15
 
8.5%

Most occurring characters

ValueCountFrequency (%)
0 161
91.5%
1 15
 
8.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 161
91.5%
1 15
 
8.5%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 161
91.5%
1 15
 
8.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 161
91.5%
1 15
 
8.5%

ofenheizung
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
0
175 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.6%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

Length

2023-07-11T16:33:12.307409image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T16:33:12.460980image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%
Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
0
156 
1
20 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 156
88.6%
1 20
 
11.4%

Length

2023-07-11T16:33:12.589141image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T16:33:12.747453image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 156
88.6%
1 20
 
11.4%

Most occurring characters

ValueCountFrequency (%)
0 156
88.6%
1 20
 
11.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 156
88.6%
1 20
 
11.4%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 156
88.6%
1 20
 
11.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 156
88.6%
1 20
 
11.4%

pantry
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
0
175 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.6%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

Length

2023-07-11T16:33:12.878699image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T16:33:13.032299image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

parkett
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
0
139 
1
37 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 139
79.0%
1 37
 
21.0%

Length

2023-07-11T16:33:13.162013image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T16:33:13.315859image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 139
79.0%
1 37
 
21.0%

Most occurring characters

ValueCountFrequency (%)
0 139
79.0%
1 37
 
21.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 139
79.0%
1 37
 
21.0%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 139
79.0%
1 37
 
21.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 139
79.0%
1 37
 
21.0%

pellet
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
0
174 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 174
98.9%
1 2
 
1.1%

Length

2023-07-11T16:33:13.447778image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T16:33:13.603185image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 174
98.9%
1 2
 
1.1%

Most occurring characters

ValueCountFrequency (%)
0 174
98.9%
1 2
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 174
98.9%
1 2
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 174
98.9%
1 2
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 174
98.9%
1 2
 
1.1%

personenaufzug
Categorical

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
0
125 
1
51 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 125
71.0%
1 51
29.0%

Length

2023-07-11T16:33:13.733376image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T16:33:13.888753image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 125
71.0%
1 51
29.0%

Most occurring characters

ValueCountFrequency (%)
0 125
71.0%
1 51
29.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 125
71.0%
1 51
29.0%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 125
71.0%
1 51
29.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 125
71.0%
1 51
29.0%

reinigung
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
0
160 
1
 
16

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 160
90.9%
1 16
 
9.1%

Length

2023-07-11T16:33:14.021274image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T16:33:14.179660image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 160
90.9%
1 16
 
9.1%

Most occurring characters

ValueCountFrequency (%)
0 160
90.9%
1 16
 
9.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 160
90.9%
1 16
 
9.1%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 160
90.9%
1 16
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 160
90.9%
1 16
 
9.1%

renoviert
Categorical

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
0
154 
1
22 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 154
87.5%
1 22
 
12.5%

Length

2023-07-11T16:33:14.313436image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T16:33:14.468881image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 154
87.5%
1 22
 
12.5%

Most occurring characters

ValueCountFrequency (%)
0 154
87.5%
1 22
 
12.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 154
87.5%
1 22
 
12.5%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 154
87.5%
1 22
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 154
87.5%
1 22
 
12.5%

rollstuhlgerecht
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
0
174 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 174
98.9%
1 2
 
1.1%

Length

2023-07-11T16:33:14.600863image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T16:33:14.756241image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 174
98.9%
1 2
 
1.1%

Most occurring characters

ValueCountFrequency (%)
0 174
98.9%
1 2
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 174
98.9%
1 2
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 174
98.9%
1 2
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 174
98.9%
1 2
 
1.1%

sat
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
0
169 
1
 
7

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 169
96.0%
1 7
 
4.0%

Length

2023-07-11T16:33:14.886472image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T16:33:15.039901image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 169
96.0%
1 7
 
4.0%

Most occurring characters

ValueCountFrequency (%)
0 169
96.0%
1 7
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 169
96.0%
1 7
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 169
96.0%
1 7
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 169
96.0%
1 7
 
4.0%

speisekammer
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
0
173 
1
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 173
98.3%
1 3
 
1.7%

Length

2023-07-11T16:33:15.171838image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T16:33:15.333401image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 173
98.3%
1 3
 
1.7%

Most occurring characters

ValueCountFrequency (%)
0 173
98.3%
1 3
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 173
98.3%
1 3
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 173
98.3%
1 3
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 173
98.3%
1 3
 
1.7%

stein
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
0
174 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 174
98.9%
1 2
 
1.1%

Length

2023-07-11T16:33:15.472438image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T16:33:15.643865image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 174
98.9%
1 2
 
1.1%

Most occurring characters

ValueCountFrequency (%)
0 174
98.9%
1 2
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 174
98.9%
1 2
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 174
98.9%
1 2
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 174
98.9%
1 2
 
1.1%

stellplatz
Categorical

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
0
152 
1
24 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 152
86.4%
1 24
 
13.6%

Length

2023-07-11T16:33:15.786009image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T16:33:15.948435image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 152
86.4%
1 24
 
13.6%

Most occurring characters

ValueCountFrequency (%)
0 152
86.4%
1 24
 
13.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 152
86.4%
1 24
 
13.6%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 152
86.4%
1 24
 
13.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 152
86.4%
1 24
 
13.6%

teilweise_m\u00f6bliert
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
0
171 
1
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 171
97.2%
1 5
 
2.8%

Length

2023-07-11T16:33:16.094593image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T16:33:16.251171image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 171
97.2%
1 5
 
2.8%

Most occurring characters

ValueCountFrequency (%)
0 171
97.2%
1 5
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 171
97.2%
1 5
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 171
97.2%
1 5
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 171
97.2%
1 5
 
2.8%

teppich
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
0
173 
1
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 173
98.3%
1 3
 
1.7%

Length

2023-07-11T16:33:16.392746image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T16:33:16.555175image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 173
98.3%
1 3
 
1.7%

Most occurring characters

ValueCountFrequency (%)
0 173
98.3%
1 3
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 173
98.3%
1 3
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 173
98.3%
1 3
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 173
98.3%
1 3
 
1.7%

terrasse
Categorical

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
0
138 
1
38 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 138
78.4%
1 38
 
21.6%

Length

2023-07-11T16:33:16.716219image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T16:33:16.879260image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 138
78.4%
1 38
 
21.6%

Most occurring characters

ValueCountFrequency (%)
0 138
78.4%
1 38
 
21.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 138
78.4%
1 38
 
21.6%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 138
78.4%
1 38
 
21.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 138
78.4%
1 38
 
21.6%

tiefgarage
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
0
165 
1
 
11

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 165
93.8%
1 11
 
6.2%

Length

2023-07-11T16:33:17.017011image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T16:33:17.179626image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 165
93.8%
1 11
 
6.2%

Most occurring characters

ValueCountFrequency (%)
0 165
93.8%
1 11
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 165
93.8%
1 11
 
6.2%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 165
93.8%
1 11
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 165
93.8%
1 11
 
6.2%

vermietet
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
0
173 
1
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 173
98.3%
1 3
 
1.7%

Length

2023-07-11T16:33:17.317766image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T16:33:17.488143image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 173
98.3%
1 3
 
1.7%

Most occurring characters

ValueCountFrequency (%)
0 173
98.3%
1 3
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 173
98.3%
1 3
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 173
98.3%
1 3
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 173
98.3%
1 3
 
1.7%

wanne
Categorical

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
0
102 
1
74 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 102
58.0%
1 74
42.0%

Length

2023-07-11T16:33:17.623223image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T16:33:17.786439image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 102
58.0%
1 74
42.0%

Most occurring characters

ValueCountFrequency (%)
0 102
58.0%
1 74
42.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 102
58.0%
1 74
42.0%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 102
58.0%
1 74
42.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 102
58.0%
1 74
42.0%

wasch_trockenraum
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
0
164 
1
 
12

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 164
93.2%
1 12
 
6.8%

Length

2023-07-11T16:33:17.928093image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T16:33:18.110038image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 164
93.2%
1 12
 
6.8%

Most occurring characters

ValueCountFrequency (%)
0 164
93.2%
1 12
 
6.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 164
93.2%
1 12
 
6.8%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 164
93.2%
1 12
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 164
93.2%
1 12
 
6.8%

wg_geeignet
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
0
166 
1
 
10

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 166
94.3%
1 10
 
5.7%

Length

2023-07-11T16:33:18.253134image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T16:33:18.417176image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 166
94.3%
1 10
 
5.7%

Most occurring characters

ValueCountFrequency (%)
0 166
94.3%
1 10
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 166
94.3%
1 10
 
5.7%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 166
94.3%
1 10
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 166
94.3%
1 10
 
5.7%

wintergarten
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
0
172 
1
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 172
97.7%
1 4
 
2.3%

Length

2023-07-11T16:33:18.557127image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T16:33:18.726007image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 172
97.7%
1 4
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 172
97.7%
1 4
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 172
97.7%
1 4
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 172
97.7%
1 4
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 172
97.7%
1 4
 
2.3%

wohnberechtigungsschein
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
0
173 
1
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 173
98.3%
1 3
 
1.7%

Length

2023-07-11T16:33:18.866582image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T16:33:19.035212image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 173
98.3%
1 3
 
1.7%

Most occurring characters

ValueCountFrequency (%)
0 173
98.3%
1 3
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 173
98.3%
1 3
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 173
98.3%
1 3
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 173
98.3%
1 3
 
1.7%

zentralheizung
Categorical

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
1
90 
0
86 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 90
51.1%
0 86
48.9%

Length

2023-07-11T16:33:19.168747image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T16:33:19.331631image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 90
51.1%
0 86
48.9%

Most occurring characters

ValueCountFrequency (%)
1 90
51.1%
0 86
48.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 90
51.1%
0 86
48.9%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 90
51.1%
0 86
48.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 90
51.1%
0 86
48.9%

Interactions

2023-07-11T16:32:49.705105image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:32:45.243842image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:32:46.125359image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:32:47.004846image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:32:47.975365image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:32:48.774444image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:32:49.854971image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:32:45.416279image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:32:46.274999image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:32:47.318785image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:32:48.119429image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:32:48.921869image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:32:49.994758image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:32:45.565814image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:32:46.421561image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:32:47.460157image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:32:48.252629image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:32:49.065999image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:32:50.126876image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:32:45.701968image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:32:46.561890image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:32:47.581518image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:32:48.376381image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:32:49.199399image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:32:50.260030image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:32:45.838218image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:32:46.707146image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:32:47.709846image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:32:48.505317image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:32:49.342368image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:32:50.401127image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:32:45.984267image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:32:46.861827image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:32:47.847840image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:32:48.647348image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T16:32:49.489345image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-07-11T16:33:19.618902image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Unnamed: 0Object_priceLivingSpaceRoomsConstructionYearZipCodeabstellraumals_ferienimmobilie_geeignetaltbau_(bis_1945)bad/wc_getrenntbalkonbarriefreibidetcarportdachgeschossdielendslduplexduscheeinbauk\u00fccheelektroerdgeschosserdwaermeerstbezugestrichetagenheizungfensterfernfernefliesenfreifu\u00dfbodenheizunggaestewcgaragegartengartennutzunggasgepflegthaustiere_erlaubtholzfensterkable_sat_tvkamerakaminkelleranteilkfw40kfw55kfw70klimatisiertkontrollierte_be-_und_entl\u00fcftungsanlagekunststoffkunststofffensterlaminatlinoleumloggialuftwpmarmormassivhausmoebliertneubauneuwertigoelofenheizungoffene_k\u00fcchepantryparkettpelletpersonenaufzugreinigungrenoviertrollstuhlgerechtsatspeisekammersteinstellplatzteilweise_m\u00f6bliertteppichterrassetiefgaragevermietetwannewasch_trockenraumwg_geeignetwintergartenwohnberechtigungsscheinzentralheizung
Unnamed: 01.0000.0400.0840.0850.034-0.0430.0540.0000.0000.0000.0000.0000.0000.0000.1710.0000.0830.0000.3840.1930.3570.1070.3870.2640.0000.1030.2770.1450.2670.3060.2980.0000.0000.2920.2310.1280.2600.0000.0000.1800.3700.0000.1630.0000.0000.0450.0450.2230.3340.0000.2980.0000.0000.0000.1900.0450.2160.0970.0000.0080.1630.0000.1150.0450.3130.0000.1980.3270.1100.0000.1270.1630.0000.1500.1580.0000.2250.2510.0000.1290.1730.0000.1180.0000.284
Object_price0.0401.0000.7970.6810.1440.0510.4700.0000.0000.3230.1930.3590.6700.5290.0870.0000.0000.0000.2060.1120.5880.0000.8090.1900.0000.1050.1670.2470.2980.2330.3780.4310.4150.5850.2600.2300.0000.0000.0000.1730.4000.4720.0000.2090.0000.0760.0000.0000.5830.0000.2980.0000.0000.0000.5030.0000.0000.0000.1540.0000.0000.0000.2290.0000.5190.2530.3810.6540.0000.6910.0000.6380.4520.0000.0800.0900.3120.0000.0000.2980.0790.0000.0000.0000.221
LivingSpace0.0840.7971.0000.8650.1080.3160.4850.0000.0000.3230.3600.4600.6700.4420.1630.0000.0780.1100.1030.1560.4870.0000.6850.0000.0000.0000.2830.1410.3070.1630.3310.4070.5300.4640.2990.2630.1880.0600.0000.2280.4170.4680.1820.3330.1100.0000.0000.0000.4450.0000.2840.0000.0470.0000.4320.0000.0000.3260.1830.0000.0000.0000.0000.0000.4940.2230.3230.6360.0000.6880.0000.3210.4510.0000.0000.1420.3120.2420.0000.3780.1580.0000.0000.0000.290
Rooms0.0850.6810.8651.0000.0800.3150.3400.0000.0000.2870.1930.1580.0000.3420.1300.2340.0000.0000.0000.1380.2900.0000.4220.0000.0000.0800.3320.2030.3270.0000.2730.2750.4500.3160.1600.0000.1770.0000.0000.0000.2350.0000.1480.3470.0000.0000.0000.0000.2670.0000.1650.0000.0000.0000.3830.0000.0000.3720.0000.0000.0000.0000.0000.0000.3350.0000.1390.3350.1400.4620.0000.5840.0000.1500.0000.0000.2190.0690.2130.4080.0000.0000.0000.0000.226
ConstructionYear0.0340.1440.1080.0801.0000.1550.1170.0000.4290.3850.1890.2800.9850.0000.0000.2490.4190.0000.0000.0350.0630.1900.1420.0000.3540.3320.2440.2720.0000.0000.0770.2830.1760.0000.0000.0520.2020.1930.1570.4520.1880.6830.0970.1971.0000.0000.0000.0000.1850.0000.0000.2230.0000.0000.3210.0000.0970.0000.3140.0580.1761.0000.1960.9850.1930.0000.3720.3410.2170.0000.2110.0000.7380.0000.4340.0000.0150.0000.0000.1610.2310.1370.0000.0000.138
ZipCode-0.0430.0510.3160.3150.1551.0000.0890.3660.0000.0820.0000.0000.0000.2790.1470.0000.0300.0000.0240.2050.0000.0000.0000.3530.0000.0000.2330.0080.0000.1070.0000.0000.0630.0490.0000.0680.0000.0000.0650.0000.1390.0000.0000.2320.0000.1470.0000.2700.0000.0000.0880.0000.1010.0690.1360.0000.0000.1110.0000.0000.2740.0000.1530.0000.0000.0000.2120.0000.0000.1410.2090.1160.0000.0000.0000.0560.1780.2100.0000.1270.0480.0000.0000.0000.117
abstellraum0.0540.4700.4850.3400.1170.0891.0000.0000.0000.2870.0850.0540.0000.0000.1180.0000.2650.0000.4070.0000.3280.0000.4130.0000.0000.0000.2030.0000.2700.3090.3410.3080.1600.3230.0000.0000.0000.1170.0000.0540.5350.1520.0000.1790.0000.0000.0000.1520.3430.1030.4020.0000.0420.1740.1490.0000.0000.1400.0000.0000.0000.0000.0750.0000.4610.1520.2280.5220.0000.0000.0000.2260.0000.1890.0000.0000.0000.0760.0920.1470.0000.0000.0000.0000.000
als_ferienimmobilie_geeignet0.0000.0000.0000.0000.0000.3660.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0170.0170.0000.0000.0000.0530.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
altbau_(bis_1945)0.0000.0000.0000.0000.4290.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0480.0250.0000.0380.0390.0000.0000.0000.0000.0000.0830.1930.2340.0000.0000.0000.0420.0000.0000.0000.0000.0000.0000.1630.0000.0000.0000.0000.1040.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1240.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0640.0000.0000.0000.0000.3020.0000.0000.000
bad/wc_getrennt0.0000.3230.3230.2870.3850.0820.2870.0000.0001.0000.0000.1130.1790.1790.0000.0000.0000.0000.1740.0000.0000.0000.0000.0000.0000.0000.0940.0000.1800.0410.0970.0810.1870.0810.0000.0000.0000.0000.0000.2490.0920.4170.0000.0000.0000.1790.0000.0000.0000.0000.0460.0000.2770.0000.0000.0000.0000.0000.0000.0000.0000.0000.2680.0000.1550.1030.0950.2000.0000.0000.0000.3300.1030.1340.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
balkon0.0000.1930.3600.1930.1890.0000.0850.0000.0000.0001.0000.0920.0000.0000.0000.0000.0000.0000.0000.0000.0000.2000.0610.0000.0000.0000.1290.0040.0000.1030.0640.0470.2570.0620.0320.0000.0640.0000.0670.0000.0000.0000.0000.2510.0000.0000.0000.0000.0910.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1640.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1830.0000.0000.2770.0000.0000.0000.0850.000
barriefrei0.0000.3590.4600.1580.2800.0000.0540.0000.0000.1130.0921.0000.1050.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2860.1520.0000.0440.1060.2490.0000.0000.0000.0000.0000.0000.0000.0000.2810.0000.0000.0000.1050.0000.0000.0000.0000.0000.0490.1700.0000.0440.0000.0000.0180.3010.0000.0000.0000.0000.0000.0000.0160.2390.0810.0000.2810.0000.0000.0160.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2130.000
bidet0.0000.6700.6700.0000.9850.0000.0000.0000.0000.1790.0000.1051.0000.0000.0000.0000.1050.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0900.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1050.0000.3410.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0530.0000.0000.0000.0000.0770.0000.0000.0000.0000.3410.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
carport0.0000.5290.4420.3420.0000.2790.0000.0000.0000.1790.0000.0000.0001.0000.0710.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0640.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0170.0000.0000.0000.0530.0000.0000.0000.0000.0770.0000.0000.0000.2730.0000.0270.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
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kunststofffenster0.2980.2980.2840.1650.0000.0880.4020.0000.0000.0460.0000.0000.0000.0000.2060.1140.3090.1140.4410.0000.3090.1560.3390.0000.0000.0000.0000.0000.2850.5170.6470.2780.0000.3950.0000.0000.0850.0000.0000.0000.3750.0000.0000.1290.0000.0000.0000.1140.4190.1461.0000.0120.1280.1280.1240.0000.0000.0920.2220.0860.0000.0000.3400.0000.2690.0000.0000.3720.0000.0000.1140.0460.0000.1490.0860.0000.0000.0890.0000.0410.1950.0000.0000.0460.000
laminat0.0000.0000.0000.0000.2230.0000.0000.0000.0000.0000.0000.0490.0000.0000.0000.0000.0490.1520.1110.0310.0490.0000.0000.0000.0000.0000.0000.0000.0000.3090.0720.1350.0000.0000.0000.0000.0000.2580.0000.1340.0600.0000.0920.1090.0000.0000.0000.0000.0690.0000.0121.0000.0000.0000.1060.0000.0000.0000.0000.0000.0820.0000.0000.0000.0000.0000.1850.0000.0000.0000.0000.0000.0000.1390.0000.0000.0000.0340.0920.0000.2010.0000.1740.0000.000
linoleum0.0000.0000.0470.0000.0000.1010.0420.0000.0000.2770.0000.1700.0000.0000.0000.0000.0000.0000.1190.0000.0000.0000.0000.0000.0000.0000.0000.1490.2530.0340.1690.0000.0000.0000.0000.0000.0000.0000.0000.1700.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1280.0001.0000.0000.0000.0000.0000.0000.0680.0000.0000.0000.0000.0000.0000.0000.0840.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.4160.081
loggia0.0000.0000.0000.0000.0000.0690.1740.0000.0000.0000.0000.0000.0000.0000.0000.0000.1700.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1690.0000.0000.0000.0000.0000.0000.0000.0000.0000.0250.0000.0000.0910.0000.0000.0000.0000.0000.0000.1280.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1360.1450.0000.1310.0000.0000.2510.0000.0000.2040.0000.0000.0000.1820.0000.0000.0000.0000.0000.0000.000
luftwp0.1900.5030.4320.3830.3210.1360.1490.0000.0000.0000.0000.0440.0000.0000.0000.0000.0000.0000.0000.1070.5190.0700.5340.3420.0000.0520.0000.0000.0000.0000.1400.3170.0000.3790.0000.0000.1900.0000.0000.0000.1320.0000.0000.1760.0000.0000.0000.0000.3990.0000.1240.1060.0000.0001.0000.0000.0000.0870.0000.0000.0000.0000.0590.0000.0840.0000.2300.2950.0000.0000.0000.0000.0000.0820.0000.0000.3210.1580.0000.0550.0000.0000.0000.0000.135
marmor0.0450.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2730.0000.0000.0000.0000.0000.0000.1790.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0410.0000.0000.0000.0000.0270.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
massivhaus0.2160.0000.0000.0000.0970.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1380.0000.0610.0000.0000.0000.0000.0000.0000.0740.0000.0000.0820.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2230.0000.0000.0000.0000.0000.0000.0000.0000.2400.0000.0000.0000.0000.0000.0001.0000.0000.0170.0000.0000.2010.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
moebliert0.0970.0000.3260.3720.0000.1110.1400.0170.0000.0000.0000.0180.0000.0000.0740.0000.0180.0000.2180.1290.0180.0740.0000.0000.0000.0000.2970.0480.0810.2040.1450.0000.0300.0580.0660.0000.2190.0670.0000.0180.0300.0000.0000.3420.0000.0000.0000.0000.0480.0000.0920.0000.0000.0000.0870.0000.0001.0000.0000.0170.0580.0000.0000.0000.0000.0000.0890.0660.1050.0000.0000.0000.0000.1160.0000.0000.0860.0000.0000.1470.0000.0000.0000.0000.000
neubau0.0000.1540.1830.0000.3140.0000.0000.0170.0000.0000.0000.3010.0000.0170.0000.0000.0880.1710.1080.0360.0000.0000.0000.0000.0170.0000.0000.0510.0720.0870.0000.1400.0000.0580.0000.0880.0000.0000.0000.0000.0000.0000.0000.0000.0000.0170.0170.0000.0000.1270.2220.0000.0680.0000.0000.0000.0170.0001.0000.1590.0580.0000.1140.0000.0000.1710.0290.0000.0260.1710.1000.1120.0000.0000.0170.0000.0350.0000.0000.0000.1640.0000.0000.2500.000
neuwertig0.0080.0000.0000.0000.0580.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0610.1240.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2010.0000.0000.0000.0860.0000.0000.0000.0000.0000.0000.0170.1591.0000.0000.0000.0660.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0800.0000.0000.0000.0000.1380.0000.0000.0000.000
oel0.1630.0000.0000.0000.1760.2740.0000.0000.0000.0000.1640.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0350.0000.0000.0000.0000.0000.0000.0000.0000.0000.2280.1070.0000.0000.0000.0000.0000.1050.0000.0000.0000.0000.0000.0000.0000.0820.0000.0000.0000.0000.0000.0580.0580.0001.0000.0000.0000.0000.0330.0000.1030.0000.0000.0000.0000.0000.0000.0420.0000.0000.0000.0000.0000.0500.0930.0000.1390.0000.137
ofenheizung0.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2010.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
offene_k\u00fcche0.1150.2290.0000.0000.1960.1530.0750.0530.0000.2680.0000.0000.0530.0530.2030.0000.1320.0000.3220.0000.0000.0000.0000.0000.0530.0590.1590.0000.2530.2590.1800.3010.0540.0000.0000.0000.1690.0190.0000.0000.0000.2020.0000.0000.0000.0530.0530.2020.2480.0000.3400.0000.0000.0000.0590.0000.0000.0000.1140.0660.0000.0001.0000.0000.1240.0000.0000.0730.0780.0000.2370.1420.2020.3460.0660.0000.0000.0530.0000.0000.1320.0000.0000.0000.000
pantry0.0450.0000.0000.0000.9850.0000.0000.0000.1240.0000.0000.0000.0000.0000.0000.0000.1050.0000.0000.0000.0000.0710.0000.0000.0000.0590.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1050.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0770.0000.0000.0000.0000.0000.0000.2010.0000.0000.0000.0000.0000.1050.0000.0000.0000.000
parkett0.3130.5190.4940.3350.1930.0000.4610.0000.0000.1550.0000.0000.0000.0000.1160.0000.2070.0000.3010.0000.2660.1160.3520.0000.0000.0000.1360.0270.3010.2520.2570.2230.2060.2570.0000.0000.0000.1400.0000.2070.3590.1210.0000.0980.0000.0000.0000.0000.3300.0000.2690.0000.0000.1360.0840.0000.0000.0000.0000.0000.0330.0000.1240.0001.0000.1210.1950.5360.0480.0000.1240.1870.0000.0000.0960.0000.0000.1010.0000.2120.0000.0000.0000.0000.098
pellet0.0000.2530.2230.0000.0000.0000.1520.0000.0000.1030.0000.0160.0000.0000.0000.0000.0160.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0410.0000.0000.1430.0000.0000.0000.0000.0000.0000.0000.0000.2300.0000.0000.0000.3410.0000.0000.0000.0000.0000.0000.0000.1450.0000.0000.0000.0000.1710.0000.0000.0000.0000.0000.1211.0000.0780.2350.0000.0000.0870.1780.0000.0000.0000.0000.0000.2960.0000.0000.0000.0000.0000.0000.000
personenaufzug0.1980.3810.3230.1390.3720.2120.2280.0000.0000.0950.0000.2390.0000.0000.0000.0000.0000.0000.0000.0600.1300.0000.2770.0000.0000.0290.0860.1410.1130.0000.1020.1400.1650.1200.0000.0630.2150.0000.0240.0000.0880.0780.0000.0480.0000.0000.0000.0000.1410.0400.0000.1850.0840.0000.2300.0000.0000.0890.0290.0000.1030.0000.0000.0000.1950.0781.0000.3350.0000.0000.0000.1390.0000.0000.0000.0000.0000.2110.0000.0000.0630.0000.0000.0000.000
reinigung0.3270.6540.6360.3350.3410.0000.5220.0000.0000.2000.0000.0810.0770.0770.1070.0000.2570.0000.2610.1460.4190.1070.5900.0000.0000.0000.0710.0000.2410.3070.4030.3210.3390.3570.0000.0000.0000.0000.0000.0810.5510.2350.0000.2540.0000.0770.0000.0000.4500.0000.3720.0000.0000.1310.2950.0000.0000.0660.0000.0000.0000.0000.0730.0770.5360.2350.3351.0000.0000.0000.0000.1720.0000.1100.0990.0000.0630.2760.0000.1760.0000.0000.0000.0000.075
renoviert0.1100.0000.0000.1400.2170.0000.0000.0000.0000.0000.0000.0000.0000.0000.0570.0000.0000.0000.1070.1550.0000.0000.0000.0000.0000.0000.1170.0000.0000.0480.0000.0000.0000.0000.0000.0000.2050.1100.0500.0000.0000.0000.0000.0000.0000.0000.0000.0000.0240.0000.0000.0000.0000.0000.0000.0410.0000.1050.0260.0000.0000.0000.0780.0000.0480.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2300.0000.0000.000
rollstuhlgerecht0.0000.6910.6880.4620.0000.1410.0000.0000.0000.0000.0000.2810.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1430.0000.0000.0160.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1710.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
sat0.1270.0000.0000.0000.2110.2090.0000.0000.0000.0000.0000.0000.0000.0000.0290.0870.2210.0000.0000.0000.0000.0000.0000.0000.1620.0000.0430.0000.0000.0000.0000.0460.0000.0000.0000.0000.1310.0000.0000.0000.0000.0870.0000.0000.0000.1620.1620.0000.1950.0000.1140.0000.0000.2510.0000.0000.0000.0000.1000.0000.0000.0000.2370.0000.1240.0870.0000.0000.0000.0001.0000.0400.0870.2030.0000.0000.0000.0000.0000.0520.0000.0000.0000.0000.000
speisekammer0.1630.6380.3210.5840.0000.1160.2260.0000.0000.3300.0000.0000.0000.2730.0000.0000.0000.0000.0800.0000.0000.0000.0000.0000.0000.0000.0760.0000.3090.1040.0000.2050.0830.0000.0000.0000.0000.0000.0000.0000.0000.1780.0000.0000.0000.2730.0000.0000.0000.0000.0460.0000.0000.0000.0000.0000.0000.0000.1120.0000.0000.0000.1420.0000.1870.1780.1390.1720.0000.0000.0401.0000.0000.1180.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
stein0.0000.4520.4510.0000.7380.0000.0000.0000.0000.1030.0000.0160.3410.0000.0000.2300.2810.0000.0000.0000.0000.0000.0000.0000.3410.0000.0000.0000.0000.0000.1470.0000.0000.0000.0000.0000.0000.0000.0000.0160.0000.2300.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2020.0000.0000.0000.0000.0000.0000.0000.0870.0001.0000.0000.1220.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
stellplatz0.1500.0000.0000.1500.0000.0000.1890.0000.0000.1340.0000.0000.0000.0270.2220.0000.0970.0000.0860.1500.0000.0000.0000.0000.0000.0000.0000.0420.0830.1840.0000.0390.0000.0520.0000.0000.0290.0510.0000.0970.0000.0000.1180.0000.0000.0270.0270.1770.0000.0000.1490.1390.0000.2040.0820.0270.0000.1160.0000.0000.0420.0000.3460.0000.0000.0000.0000.1100.0000.0000.2030.1180.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.2040.0000.000
teilweise_m\u00f6bliert0.1580.0800.0000.0000.4340.0000.0000.0000.0000.0000.0000.0000.0000.0000.0910.1220.2840.0000.0000.0000.0000.0910.0000.0000.2010.2030.1430.0000.0820.0000.0000.0000.0000.0000.0000.0000.1480.1520.0000.0000.1250.0000.0000.0000.0000.0000.0000.0000.0000.0000.0860.0000.0000.0000.0000.0000.0000.0000.0170.0000.0000.0000.0660.2010.0960.0000.0000.0990.0000.0000.0000.0000.1220.0001.0000.0800.0000.0000.0000.0000.0000.0000.0000.0000.000
teppich0.0000.0900.1420.0000.0000.0560.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0830.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0800.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0801.0000.0000.0000.0000.0000.0000.0000.0000.0000.039
terrasse0.2250.3120.3120.2190.0150.1780.0000.0000.0640.0000.1830.0000.0000.0000.0000.0000.0000.0000.0000.0000.1410.2130.0000.1850.0000.0000.1200.1050.1970.0000.0000.1350.0000.1430.3800.2590.0000.0000.0000.0000.0000.0000.0000.2060.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.3210.0000.0000.0860.0350.0000.0000.0000.0000.0000.0000.0000.0000.0630.0000.0000.0000.0000.0000.0000.0000.0001.0000.2230.0000.1350.0000.0640.1320.0000.000
tiefgarage0.2510.0000.2420.0690.0000.2100.0760.0000.0000.0000.0000.0000.0000.0000.0000.0000.1450.0000.0000.0000.0000.0860.0000.2820.0000.0000.0000.0000.0750.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0600.0340.0000.1790.0000.1140.0000.0000.0000.0000.0890.0340.0000.1820.1580.0000.0000.0000.0000.0000.0000.0000.0530.0000.1010.2960.2110.2760.0000.0000.0000.0000.0000.0000.0000.0000.2231.0000.0000.0670.0000.0000.0000.0000.045
vermietet0.0000.0000.0000.2130.0000.0000.0920.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1560.0000.0000.0000.0000.0000.0000.0000.0000.1070.0000.2130.0830.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0920.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.1030.0000.039
wanne0.1290.2980.3780.4080.1610.1270.1470.0000.0000.0000.2770.0000.0000.0000.0000.0000.0000.0000.0000.0000.1390.0520.1960.1120.0000.0000.3540.0000.1290.0800.1290.2120.3390.2010.0820.0830.1810.0350.0000.0000.1470.0000.0000.3220.0000.0000.0000.0000.1820.0000.0410.0000.0000.0000.0550.0000.0000.1470.0000.0000.0500.0000.0000.0000.2120.0000.0000.1760.0000.0000.0520.0000.0000.0000.0000.0000.1350.0670.0001.0000.0000.1460.1190.0000.000
wasch_trockenraum0.1730.0790.1580.0000.2310.0480.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2810.1890.0000.0000.1620.0000.0250.0000.0440.0000.0000.0000.1260.0440.0000.0000.0000.0000.0000.0000.1560.0000.2280.1860.0000.0000.1460.0000.0000.0000.0000.0000.2490.1950.2010.0000.0000.0000.0000.0000.0000.1640.1380.0930.0000.1320.1050.0000.0000.0630.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.000
wg_geeignet0.0000.0000.0000.0000.1370.0000.0000.0000.3020.0000.0000.0000.0000.0000.0000.0000.0000.0000.1680.1110.0000.0000.0000.0000.0000.0000.0780.0000.0000.0000.0000.0420.0000.0000.0000.0250.0000.0000.0000.0000.0000.0000.0000.1040.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2300.0000.0000.0000.0000.0000.0000.0000.0640.0000.0000.1460.0001.0000.0000.0000.000
wintergarten0.1180.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1230.0000.0000.0000.0000.0000.0000.1150.1440.0000.0000.0000.0000.2740.0000.0000.0620.0000.0000.0000.0000.1030.0000.0000.0000.0000.0000.0000.0000.0000.1740.0000.0000.0000.0000.0000.0000.0000.0000.1390.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2040.0000.0000.1320.0000.1030.1190.0000.0001.0000.0000.000
wohnberechtigungsschein0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0850.2130.0000.0000.0000.0000.0000.0000.0000.0320.0000.0000.0000.0000.0000.0000.0000.1910.1560.0000.0870.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0460.0000.4160.0000.0000.0000.0000.0000.2500.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.000
zentralheizung0.2840.2210.2900.2260.1380.1170.0000.0000.0000.0000.0000.0000.0000.0000.0380.0000.0000.0000.0000.0000.2430.0000.1630.0000.0000.3300.0000.1190.0000.0100.0000.2330.0000.1970.0000.0000.0760.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2700.0000.0000.0000.0810.0000.1350.0000.0000.0000.0000.0000.1370.0000.0000.0000.0980.0000.0000.0750.0000.0000.0000.0000.0000.0000.0000.0390.0000.0450.0390.0000.0000.0000.0000.0001.000

Missing values

2023-07-11T16:32:50.857569image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-11T16:32:51.538473image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Unnamed: 0Object_priceLivingSpaceRoomsConstructionYearZipCodeEstateTypeDistributionTypeabstellraumals_ferienimmobilie_geeignetaltbau_(bis_1945)bad/wc_getrenntbalkonbarriefreibidetcarportdachgeschossdielendslduplexduscheeinbauk\u00fccheelektroerdgeschosserdwaermeerstbezugestrichetagenheizungfensterfernfernefliesenfreifu\u00dfbodenheizunggaestewcgaragegartengartennutzunggasgepflegthaustiere_erlaubtholzfensterkable_sat_tvkamerakaminkelleranteilkfw40kfw55kfw70klimatisiertkontrollierte_be-_und_entl\u00fcftungsanlagekunststoffkunststofffensterlaminatlinoleumloggialuftwpmarmormassivhausmoebliertneubauneuwertigoelofenheizungoffene_k\u00fcchepantryparkettpelletpersonenaufzugreinigungrenoviertrollstuhlgerechtsatspeisekammersteinstellplatzteilweise_m\u00f6bliertteppichterrassetiefgaragevermietetwannewasch_trockenraumwg_geeignetwintergartenwohnberechtigungsscheinzentralheizung
00995.050.01.52012.097084APARTMENTRENT0000100000001100000000010100000000000000000000000001000000000000000000000000000
11475.050.02.0NaN97209APARTMENTRENT0000000000000101000100000000000100000100000001000000000000000000000000000110000
22750.070.02.01956.097297APARTMENTRENT0000000000000100000010000000001000000000000000001000000000000010000000000101001
33580.074.03.01940.097249APARTMENTRENT0010000000000110000010000000100000000100000000000000000000000010000000000001000
44780.086.03.01995.097297APARTMENTRENT0000100000001100000010000010000000000100000000000000011000000000000001000100001
55820.094.03.0NaN97297APARTMENTRENT0000000000000000000000000010111000000100000000000000000000000000000000000000000
661250.0115.03.52019.097246APARTMENTRENT0000110000000000000001000110000000000100000000000000100000001001000000000000000
77853.067.02.02021.097209APARTMENTRENT1000000010101000000000010100001000100000010010000000000010000000000100000000001
88836.079.02.02021.097209APARTMENTRENT1000000010101100000000010100001000100000010010000000000010000000000100000000001
991040.072.02.02019.097209APARTMENTRENT1000100000001100010010010000001000000000000000000000000000100000000100000100000
Unnamed: 0Object_priceLivingSpaceRoomsConstructionYearZipCodeEstateTypeDistributionTypeabstellraumals_ferienimmobilie_geeignetaltbau_(bis_1945)bad/wc_getrenntbalkonbarriefreibidetcarportdachgeschossdielendslduplexduscheeinbauk\u00fccheelektroerdgeschosserdwaermeerstbezugestrichetagenheizungfensterfernfernefliesenfreifu\u00dfbodenheizunggaestewcgaragegartengartennutzunggasgepflegthaustiere_erlaubtholzfensterkable_sat_tvkamerakaminkelleranteilkfw40kfw55kfw70klimatisiertkontrollierte_be-_und_entl\u00fcftungsanlagekunststoffkunststofffensterlaminatlinoleumloggialuftwpmarmormassivhausmoebliertneubauneuwertigoelofenheizungoffene_k\u00fcchepantryparkettpelletpersonenaufzugreinigungrenoviertrollstuhlgerechtsatspeisekammersteinstellplatzteilweise_m\u00f6bliertteppichterrassetiefgaragevermietetwannewasch_trockenraumwg_geeignetwintergartenwohnberechtigungsscheinzentralheizung
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